Acceleration technique for boosting classiﬁcation and its application to face detection

Abstract

We propose an acceleration technique for boosting classiﬁcation without any loss of
classiﬁcation accuracy and apply it to a face detection task. In classiﬁcation task, much eﬀort has
been spent on improving the classiﬁcation accuracy and the computational cost of training. In
addition to them, the computational cost of classiﬁcation itself can be critical in several
applications including face detection. In face detection, a celebrating work by Viola and
Jones (2001) developed a signiﬁcantly fast face detector achieving a competitive accuracy with all
preceding face detectors. In their algorithm, the cascade structure of boosting classiﬁer plays
an important role. In this paper, we propose an acceleration technique for boosting
classiﬁer. The key idea of our proposal is the fact that one can determine the sign of
discriminant function before all weak learners are evaluated in general. An advantage is that
our algorithm has no loss in classiﬁcation accuracy. Another advantage is that our
proposal is a unsupervised learning so that it can treat a covariate shift situation. We
also apply our proposal to each cascaded boosting classiﬁer in Viola and Jones type
face detector. As a result, our proposal succeeds in reducing the classiﬁcation cost by
20%.